MEI-56606 Machine Vision, 5 cr
Additional information
This course will focus on industrial applications of Machine Vision, and especially from production automation point-of-view.
Course will include large amounts of independent studies in the form of reading texts (book chapters, articles, etc.), performing tasks on-line in Moodle, writing essays, etc. In addition, course will include hands-on work in laboratory.
Person responsible
Jussi Halme, Minna Lanz
Lessons
Implementation 1: MEI-56606 2015-01
| Study type | P1 | P2 | P3 | P4 | Summer |
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Requirements
Completed and accepted assignment, essays and exercises about topics discussed during the lectures. Course has no exam.
Completion parts must belong to the same implementation
Learning Outcomes
After completing the course student has know-how about industrial machine vision at the level that he/she can analyse simple applications from machine vision point-of-view, select suitable machine vision hardware components for the application, and program the machine vision equipment. Student can also analyse how reliable the measurement/inspection results are and identify error sources.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Machine vision in production automation: Typical applications. Typical system structures. Commonly used 3D imaging methods. | ||
| 2. | Machine vision hardware: * Different system types (PC based system, smart camera based system) * Camera types and selection principles: Specifying camera resolution (field-of-view, spatial resolution) and resulting expected measurement resolution. * Lenses and other optical components: Specifying lens focal length. * Illumination in machine vision: Importance of illumination concerning the resulting image. Illumination methods and light sources. | Typical color camera vs. grey-scale camera. Shutter types. Concepts of depth-of-focus/depth-of-field and optical resolution. Effect of different light colors. | |
| 3. | Machine vision software and image processing: Digital image. Typical functionality and special properties of machine vision software. Common programming concepts and methods in machine vision. | Understanding basic operating principles of the most commonly used machine vision software algorithms. Programming simple machine vision application. | |
| 4. | Typical machine vision applications/tasks in production automation: Checking the presence/counting parts - methods Locating parts for robot pickup - robot and machine vision calibration Dimensional measurements - measurement accuracy and/or uncertainty | Communicating with other equipment. Calibrating machine vision system and combining camera and robot coordinates. Calculating measurement uncertainty. |
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | Machine Vision Handbook | Bruce G. Batchelor | ISBN: 978-1-84996-168-4 (Print | TUT has one paper copy at library (only short term loans) and license to eBook, see link. | No | |
| Lecture slides | Lecture materials etc. | Lecturer(s) | Lecture slides, exercise problems, assignment instructions, etc. | No |
Additional information about prerequisites
This course has no formal prerequisites, but since this is a Master's level course, we expect that students have some previous knowledge about production automation. Elementary know-how on programming will help completing course tasks, but is not strictly required.
Correspondence of content
There is no equivalence with any other courses